This is the Linux app named xFormers whose latest release can be downloaded as v0.0.32.post2sourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.
Download and run online this app named xFormers with OnWorks for free.
Volg deze instructies om deze app uit te voeren:
- 1. Download deze applicatie op uw pc.
- 2. Voer in onze bestandsbeheerder https://www.onworks.net/myfiles.php?username=XXXXX in met de gebruikersnaam die u wilt.
- 3. Upload deze applicatie in zo'n bestandsbeheerder.
- 4. Start de OnWorks Linux online of Windows online emulator of MACOS online emulator vanaf deze website.
- 5. Ga vanuit het OnWorks Linux-besturingssysteem dat u zojuist hebt gestart naar onze bestandsbeheerder https://www.onworks.net/myfiles.php?username=XXXXX met de gewenste gebruikersnaam.
- 6. Download de applicatie, installeer hem en voer hem uit.
SCREENSHOTS
Ad
xFormers
PRODUCTBESCHRIJVING
xformers is a modular, performance-oriented library of transformer building blocks, designed to allow researchers and engineers to compose, experiment, and optimize transformer architectures more flexibly than monolithic frameworks. It abstracts components like attention layers, feedforward modules, normalization, and positional encoding, so you can mix and match or swap optimized kernels easily. One of its key goals is efficient attention: it supports dense, sparse, low-rank, and approximate attention mechanisms (e.g. FlashAttention, Linformer, Performer) via interchangeable modules. The library includes memory-efficient operator implementations in both Python and optimized C++/CUDA, ensuring that performance isn’t sacrificed for modularity. It also integrates with PyTorch seamlessly so you can drop in its blocks to existing models, replace default attention layers, or build new architectures from scratch. xformers includes training, deployment, and memory profiling tools.
Kenmerken
- Modular transformer building blocks (attention, FFN, norms, position encodings)
- Support for various efficient attention types (sparse, approximate, locality)
- Optimized GPU kernels and fallback Python implementations
- Seamless integration with PyTorch models and training loops
- Profiling and benchmarking tools to compare throughput, memory, and latency
- Support for mixing attention types in one model (hybrid architectures)
Programmeertaal
Python
Categorieën
This is an application that can also be fetched from https://sourceforge.net/projects/xformers.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.
